_base_ = [
    "../_base_/datasets/coco_detection.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime.py",
]
# model settings
model = dict(
    type="FCOS",
    # pretrained='open-mmlab://detectron/resnet50_caffe',
    pretrained=None,
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs=True,
        extra_convs_on_inputs=False,  # use P5
        num_outs=5,
        relu_before_extra_convs=True,
    ),
    bbox_head=dict(
        type="FCOSHead",
        num_classes=80,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="IoULoss", loss_weight=1.0),
        loss_centerness=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
        ),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0,
            ignore_iof_thr=-1,
        ),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type="nms", iou_threshold=0.5),
        max_per_img=100,
    ),
)
img_norm_cfg = dict(
    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
# optimizer
optimizer = dict(lr=0.01, paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
    policy="step",
    warmup="constant",
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    step=[8, 11],
)
runner = dict(type="EpochBasedRunner", max_epochs=12)
